---
title: Data Platform Strategy
summary: Architectural decision record for adopting a modern data platform at FinSecure
sidebar:
    label: Data Platform Strategy
    order: 2
---

## ADR-2023-12: Modern Data Platform Strategy for FinSecure

### Status

Approved (2023-12-18)

### Context

FinSecure is experiencing significant challenges with our current data architecture:

1. **Data Silos**: Customer, transaction, and risk data are siloed across multiple legacy systems with inconsistent data models.

2. **Limited Analytics Capabilities**: Rigid data warehousing solutions limit our ability to perform advanced analytics and machine learning.

3. **Scalability Constraints**: Current data processing infrastructure is struggling to handle increasing data volumes (now exceeding 5TB daily).

4. **Compliance Complexity**: Meeting GDPR, CCPA, and financial regulatory requirements across fragmented data systems is increasingly difficult.

5. **Slow Time-to-Insight**: Business teams wait 2-3 weeks for new analytics dashboards or data models to be developed.

6. **Technical Debt**: Legacy ETL processes are complex, brittle, and expensive to maintain.

7. **Limited Real-time Capabilities**: Current architecture is primarily batch-oriented with limited ability to process streaming data for fraud detection and real-time decisioning.

8. **Data Quality Issues**: Inconsistent data quality across systems impacts business decisions and customer experience.

These challenges are limiting our ability to leverage data as a strategic asset and inhibiting our digital transformation initiatives aimed at enhancing customer experiences and operational efficiency.

### Decision

We will implement a **modern, cloud-based data platform** with a **lakehouse architecture**. Key components include:

1. **Data Lake Foundation**:
   - Azure Data Lake Storage Gen2 as the foundation for our data lake
   - Databricks Delta Lake for ACID transactions and data reliability
   - Structured organization with bronze (raw), silver (refined), and gold (business) layers

2. **Data Ingestion and Processing**:
   - Azure Data Factory for orchestration and batch data movement
   - Kafka and Azure Event Hubs for real-time data ingestion
   - Databricks for large-scale data processing
   - Stream processing with Spark Structured Streaming

3. **Semantic Layer and Data Serving**:
   - Databricks SQL Warehouses for analytics workloads
   - Azure Synapse Analytics for enterprise data warehousing needs
   - Power BI as primary business intelligence tool
   - REST APIs for serving data to applications

4. **Data Governance and Security**:
   - Azure Purview for data catalog and lineage
   - Column-level encryption for sensitive data
   - Role-based access control aligned with data classification
   - Automated data retention and purging based on policies

5. **Machine Learning Platform**:
   - MLflow for experiment tracking and model registry
   - Databricks ML for model development and deployment
   - Model monitoring and retraining pipelines
   - Feature store for reusable feature engineering

6. **DataOps and Automation**:
   - Infrastructure as Code using Terraform
   - CI/CD pipelines for data pipelines and transformations
   - Automated testing for data quality and pipeline integrity
   - Comprehensive monitoring and alerting

### Platform Architecture by Domain

| Domain | Data Types | Primary Tools | Access Patterns | Special Requirements |
|--------|------------|--------------|-----------------|----------------------|
| Customer 360 | Customer profiles, interactions, preferences | Delta Lake, Databricks SQL | Batch analytics, Real-time lookups | GDPR compliance, Entity resolution |
| Transaction Processing | Payment transactions, transfers, statements | Kafka, Delta Lake, Azure Synapse | Real-time streaming, Batch reporting | PCI-DSS compliance, 7-year retention |
| Risk Management | Credit scores, market data, exposure calculations | Databricks, Delta Lake, ML models | Batch processing, Model inference | Auditability, Model governance |
| Fraud Detection | Transaction patterns, behavioral signals | Kafka, Spark Streaming, ML models | Real-time streaming, Low-latency scoring | Sub-second latency, High availability |
| Regulatory Reporting | Aggregated financial data, compliance metrics | Azure Synapse, Power BI | Scheduled batch, Ad-hoc analysis | Immutability, Approval workflows |
| Marketing Analytics | Campaign data, customer segments, attribution | Databricks, Delta Lake, Power BI | Interactive queries, ML-based segmentation | Identity resolution, Attribution models |

### Consequences

#### Positive

1. **Unified Data Access**: Single platform for accessing enterprise data with consistent governance.

2. **Enhanced Analytical Capabilities**: Support for advanced analytics, machine learning, and AI initiatives.

3. **Improved Scalability**: Cloud-native architecture can scale to handle growing data volumes.

4. **Reduced Time-to-Insight**: Self-service capabilities and streamlined data pipelines reduce time to deliver insights.

5. **Better Data Governance**: Centralized data catalog, lineage tracking, and security controls.

6. **Real-time Capabilities**: Support for both batch and real-time processing using the same platform.

7. **Cost Optimization**: Pay-for-use cloud model with ability to scale resources as needed.

8. **Regulatory Compliance**: Improved ability to implement and demonstrate regulatory compliance.

#### Negative

1. **Implementation Complexity**: Significant effort required to migrate from legacy systems.

2. **Skills Gap**: New technologies require reskilling of existing teams.

3. **Initial Cost Increase**: Short-term investment in new technology and parallel running of systems.

4. **Data Migration Challenges**: Data quality and mapping issues during migration.

5. **Operational Changes**: New operational procedures and support models needed.

6. **Integration Complexity**: Connecting legacy systems to new platform requires careful planning.

7. **Organization Change Management**: New workflows and responsibilities across business and technical teams.

### Mitigation Strategies

1. **Phased Implementation Approach**:
   - Start with highest-value, least-critical data domains
   - Implement foundational capabilities before complex use cases
   - Run legacy and new systems in parallel during transition
   - Create clear success criteria for each phase

2. **Talent and Skill Development**:
   - Develop comprehensive training program for existing staff
   - Strategic hiring for key specialized roles
   - Partner with platform vendors for enablement
   - Create centers of excellence for key technologies

3. **Modern Data Governance**:
   - Establish data governance council with cross-functional representation
   - Define clear data ownership and stewardship model
   - Implement automated data quality monitoring
   - Create comprehensive data classification framework

4. **Financial Management**:
   - Detailed cloud cost monitoring and optimization
   - Business-aligned chargeback model
   - Clear ROI tracking for data initiatives
   - Regular cost optimization reviews

5. **Change Management Program**:
   - Executive sponsorship and visible leadership
   - Regular communication and success stories
   - Early involvement of business stakeholders
   - Incentives aligned with adoption goals

### Implementation Details

#### Phase 1: Foundation (Q1-Q2 2024)

1. Establish cloud environment and core infrastructure
2. Implement data lake foundation with initial data domains
3. Deploy data catalog and basic governance tools
4. Migrate first non-critical data workloads
5. Establish DataOps practices and pipelines

#### Phase 2: Expansion (Q3-Q4 2024)

1. Migrate core analytical workloads to the platform
2. Implement real-time data processing capabilities
3. Deploy self-service analytics for business users
4. Enhance data quality frameworks and monitoring
5. Develop initial ML use cases on the platform

#### Phase 3: Advanced Capabilities (Q1-Q2 2025)

1. Full enterprise adoption across all data domains
2. Advanced ML capabilities and feature store
3. Comprehensive data governance implementation
4. Legacy system decommissioning
5. Advanced real-time analytics and decisioning

### Considered Alternatives

#### 1. Modernize Existing Data Warehouse

**Pros**: Lower initial disruption, familiar technology, focused scope  
**Cons**: Limited flexibility, higher long-term costs, limited real-time capabilities

This approach would not address our fundamental needs for real-time processing, advanced analytics, and managing unstructured data.

#### 2. Traditional Data Lake Architecture

**Pros**: Lower cost storage, support for varied data types, scalability  
**Cons**: Complexity in ensuring data quality, limited transactional support, governance challenges

A traditional data lake without the lakehouse capabilities would create significant challenges for data reliability, performance, and governance.

#### 3. Multiple Purpose-Built Systems

**Pros**: Optimized solutions for specific use cases, potentially best-in-class capabilities  
**Cons**: Increased integration complexity, data duplication, inconsistent governance

This approach would perpetuate our data silo issues and create ongoing integration and consistency challenges.

#### 4. Maintain and Incrementally Improve Current Systems

**Pros**: Minimal disruption, lower initial investment, familiar technology  
**Cons**: Perpetuates technical debt, limited capability improvement, increasing maintenance costs

This would fail to address our fundamental challenges and put us at a competitive disadvantage as data volumes and complexity increase.

### References

1. "Designing Data-Intensive Applications" by Martin Kleppmann
2. [Databricks Lakehouse Platform Documentation](https://docs.databricks.com/lakehouse/index.html)
3. [Azure Data Factory Documentation](https://docs.microsoft.com/en-us/azure/data-factory/)
4. "Data Mesh: Delivering Data-Driven Value at Scale" by Zhamak Dehghani
5. FinSecure Internal Report: "Data Platform Requirements Analysis" (October 2023)
6. [DAMA Data Management Body of Knowledge](https://www.dama.org/cpages/body-of-knowledge)

### Decision Record History

| Date | Version | Description | Author |
|------|---------|-------------|--------|
| 2023-10-15 | 0.1 | Initial draft | Jennifer Wu, Chief Data Officer |
| 2023-11-08 | 0.2 | Updated based on technical review | Raj Patel, Data Engineering Lead |
| 2023-12-02 | 0.3 | Added implementation phases and cost estimates | Michael Torres, Enterprise Architect |
| 2023-12-18 | 1.0 | Approved by Executive Technology Committee | FinSecure ETC |

## Appendix A: Data Platform Architecture

```mermaid
flowchart TB
    subgraph "Data Sources"
        core[Core Banking Systems]
        crm[CRM Systems]
        channels[Digital Channels]
        market[Market Data Feeds]
        apps[Applications]
        ext[External Partners]
    end
    
    subgraph "Data Ingestion"
        batch[Batch Processing]
        stream[Stream Processing]
        cdc[Change Data Capture]
        api[API Integration]
    end
    
    subgraph "Data Storage"
        subgraph "Data Lake"
            bronze[Bronze Layer<br>Raw Data]
            silver[Silver Layer<br>Cleansed & Conformed]
            gold[Gold Layer<br>Business Aggregates]
        end
        
        dw[Enterprise Data Warehouse]
        fs[Feature Store]
    end
    
    subgraph "Processing & Analytics"
        etl[ETL/ELT Processes]
        bi[Business Intelligence]
        ml[Machine Learning]
        ad[Advanced Analytics]
        realtime[Real-time Analytics]
    end
    
    subgraph "Data Consumption"
        reports[Reports & Dashboards]
        apps2[Applications]
        apis[APIs & Services]
        exports[Exports & Feeds]
        ml_svc[ML Services]
    end
    
    subgraph "Governance & Operations"
        catalog[Data Catalog]
        quality[Data Quality]
        lineage[Data Lineage]
        security[Security & Privacy]
        lifecycle[Data Lifecycle]
    end
    
    core --> batch
    crm --> batch
    channels --> stream
    market --> stream
    apps --> cdc
    ext --> api
    
    batch --> bronze
    stream --> bronze
    cdc --> bronze
    api --> bronze
    
    bronze --> etl
    etl --> silver
    silver --> etl
    etl --> gold
    etl --> dw
    gold --> dw
    silver --> fs
    
    dw --> bi
    gold --> bi
    silver --> ml
    gold --> ml
    fs --> ml
    silver --> ad
    bronze --> realtime
    silver --> realtime
    
    bi --> reports
    ml --> ml_svc
    ad --> apis
    realtime --> apps2
    dw --> exports
    
    catalog --> bronze
    catalog --> silver
    catalog --> gold
    catalog --> dw
    
    quality --> silver
    lineage --> etl
    security --> dw
    lifecycle --> bronze
```

## Appendix B: Data Platform Implementation Timeline

```mermaid
gantt
    title FinSecure Data Platform Implementation
    dateFormat  YYYY-MM-DD
    
    section Strategy & Planning
    Requirements Analysis       :done, 2023-08-01, 2023-10-15
    Vendor Evaluation           :done, 2023-09-15, 2023-11-15
    Architecture Design         :done, 2023-10-01, 2023-12-15
    
    section Phase 1: Foundation
    Core Infrastructure Setup   :active, 2024-01-15, 2024-02-28
    Data Lake Implementation    :2024-02-01, 2024-03-31
    Governance Framework        :2024-02-15, 2024-04-15
    Initial Data Migration      :2024-03-01, 2024-05-31
    DataOps Implementation      :2024-04-01, 2024-06-30
    
    section Phase 2: Expansion
    Advanced Analytics Platform :2024-07-01, 2024-09-30
    Streaming Data Processing   :2024-08-01, 2024-10-31
    Self-service Analytics      :2024-09-01, 2024-11-30
    Data Quality Framework      :2024-10-01, 2024-12-15
    ML Platform Deployment      :2024-10-15, 2025-01-31
    
    section Phase 3: Advanced
    Enterprise-wide Adoption    :2025-02-01, 2025-04-30
    Legacy System Decomm        :2025-03-01, 2025-06-30
    Advanced Governance         :2025-04-01, 2025-06-30
    Real-time Decisioning       :2025-05-01, 2025-07-31
    Program Completion          :milestone, 2025-07-31
```

## Appendix C: Target State Data Flow - Customer 360 Example

```mermaid
sequenceDiagram
    participant Source as Source Systems
    participant Ingest as Data Ingestion
    participant Bronze as Bronze Layer
    participant Process as Data Processing
    participant Silver as Silver Layer
    participant Gold as Gold Layer
    participant Consume as Data Consumption
    
    Source->>Ingest: Customer Data<br>(Core Banking, CRM, Channels)
    Ingest->>Bronze: Raw Data Ingestion<br>(Batch & Streaming)
    
    Bronze->>Process: Validation & Cleansing
    Process->>Process: Data Quality Checks
    Process->>Process: Deduplication
    Process->>Process: Standardization
    Process->>Silver: Refined Customer Records
    
    Silver->>Process: Entity Resolution
    Process->>Process: Customer Attribute Generation
    Process->>Process: Relationship Mapping
    Process->>Process: History Processing
    Process->>Gold: Unified Customer Profiles
    
    Gold->>Consume: Customer 360 Views
    Gold->>Consume: Segmentation Models
    Gold->>Consume: Marketing Analytics
    Gold->>Consume: Risk Assessment
    Gold->>Consume: Real-time Personalization
    
    Note over Bronze,Gold: Data Governance<br>- Lineage Tracking<br>- Privacy Controls<br>- Data Classification<br>- Quality Monitoring
```

## Appendix D: Key Performance Indicators

| KPI | Current State | Target (2025) | Measurement Method |
|-----|---------------|---------------|-------------------|
| Data Integration Cycle Time | 7-14 days | &lt;24 hours | Average time from source change to data availability |
| Self-service BI Adoption | 15% of business users | &gt;60% of business users | Monthly active users in self-service tools |
| Data Quality Score | ~75% | &gt;95% | Composite score from automated quality checks |
| Cost per TB of Analytics Storage | $2,500/TB | &lt;$500/TB | Total cost of ownership / storage volume |
| Time to New Analytics | 2-3 weeks | &lt;3 days | Time from request to dashboard availability |
| Data Platform Availability | 99.5% | 99.95% | Measured service uptime |
| Regulatory Report Production Time | 10-15 days | 1-2 days | Time to produce monthly regulatory reports |
| Real-time Decision Latency | Not available | &lt;250ms | Response time for real-time decision APIs |
| ML Model Deployment Time | 4-6 weeks | &lt;1 week | Time from model approval to production deployment |
| Data Engineer Productivity | ~30% on new features | &gt;70% on new features | Time allocation analysis |

*Note: Metrics will be tracked quarterly and reported to the Data Governance Council.* 